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However, popular clustering-based point cloud segmentation methods are usually only suitable for pure forest scenes and not ideal for scenes with multiple ground features or complex terrain. Therefore, this study proposes a single-tree point cloud extraction method that combines deep semantic segmentation and clustering. This method first uses a deep semantic segmentation network, Improved-RandLA-Net, which is developed based on RandLA-Net, to extract point clouds of specified tree species by adding an attention chain to improve the model\u2019s ability to extract channel and spatial features. Subsequently, clustering is employed to extract single-tree point clouds from the segmented point clouds. The feasibility of the proposed method was verified in the Gingko site, the Lin\u2019an Pecan site, and a Fraxinus excelsior site in a conference center. Finally, semantic segmentation was performed on three sample areas using pre- and postimproved RandLA-Net. The experiments demonstrate that Improved-RandLA-Net had significant improvements in Accuracy, Precision, Recall, and F1 score. At the same time, based on the semantic segmentation results of Improved-RandLA-Net, single-tree point clouds of three sample areas were extracted, and the final single-tree recognition rates for each sample area were 89.80%, 75.00%, and 95.39%, respectively. The results demonstrate that our proposed method can effectively extract single-tree point clouds in complex scenes.<\/jats:p>","DOI":"10.3390\/rs15102644","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:55:29Z","timestamp":1684457729000},"page":"2644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Study on Single-Tree Extraction Method for Complex RGB Point Cloud Scenes"],"prefix":"10.3390","volume":"15","author":[{"given":"Kai","family":"Xia","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China"}]},{"given":"Yinhui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China"}]},{"given":"Susu","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Science, Zhejiang A & F University, Hangzhou 311300, China"}]},{"given":"Hailin","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lister, A.J., Andersen, H., Frescino, T., Gatziolis, D., Healey, S., Heath, L.S., Liknes, G.C., McRoberts, R., Moisen, G.G., and Nelson, M. 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